A key part of most electronic commerce solutions involves the ability to quickly locate products and services and tailor content to each individual user or customer. This requires both product indexing and user profiling.
There are many search engines and knowledge management tools, but none of these are built using the Enterprise Java Beans architecture. The present invention provides a mechanism by which a searchable product inventory can be matched with a customer's preference. It does so in a way that is simple for the programmer, scalable in design, and built upon an open systems architecture.
Normally search engines require a large amount of memory space to execute efficiently. This algorithm is designed to take advantage of the object caching built into the underlying platform such that it has a smaller footprint.
Normally search engines count the number of instances of words in html documents and then cross reference, by word count so that documents with the most number of word matches can be quickly found. The Advisor indexes the items in the various catalogs and makes them searchable by quality strings.
The present insertion has combined these two mechanisms into a single integrated solution called the Advisor or the Shopping Advisor component, which can be part of a suite of Enterprise Java Beans components that enable the rapid deployment of eCommerce Web Sites. For example, other components in the suite can provide support for the management of user sessions and creation of customer orders.